dTwin4SkullShapes: a Digital Twin Dataset of Human Skull Shapes for Skull Missing Vs. Existing Part Learning and Prediction
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2024
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| author | NGUYEN, Tan-Nhu VO, Phong-Phu TRAN, Vi-Do NGUYEN, Hoai-Nam PHAM, Hoang-Anh LE-NGOC, Hong-An NGUYEN, Thi-Tuong-Vi HUYNH, Khanh-Linh LE, Ngoc-Bich NGUYEN, Thi-Hiep DAO, Tien-Tuan |
| author_facet | NGUYEN, Tan-Nhu VO, Phong-Phu TRAN, Vi-Do NGUYEN, Hoai-Nam PHAM, Hoang-Anh LE-NGOC, Hong-An NGUYEN, Thi-Tuong-Vi HUYNH, Khanh-Linh LE, Ngoc-Bich NGUYEN, Thi-Hiep DAO, Tien-Tuan |
| contents | <p>This dataset include the total of 757 human skulls reconstructed from computed tomography (CT) image sets. The CT image sets were constructed from the three main databases: (1) The Cancer Imaging Archive (DOI: 10.7937/TCIA.HMQ8-J677), (2) The New Mexico Decedent CT Images (DOI: 10.1055/s-0041-1730999), and (3) The MGU500+ (DOI: 10.1016/j.dib.2021.107524). The selected skull image sets have normal and full skull structures. We employed the voxelization technique to reconstruct skull meshes from CT image slices of each subject. The skull meshes were stored in *.off files. For each skull mesh, we estimated the skull shape by estimating the alpha shape of the skull meshes and stored in *.off files. The feature points on the skulls were also manually picked and stored in *.csv files with the form of 16x3 matrices, in which 16 is the number of feature points. A template skull shape was also deformed to the skull shapes of all subjects and stored in *.off files. The deformed skull shapes have unified skull feature points throughout all subjects. Moreover, we also added the source codes of the project. The details of the dataset were presented in the ReadMe.txt file. More details of how to use the source code project and the dataset were presented in the dTwin4SkullShapeTutorials.pdf file.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_12729483 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | dTwin4SkullShapes: a Digital Twin Dataset of Human Skull Shapes for Skull Missing Vs. Existing Part Learning and Prediction NGUYEN, Tan-Nhu VO, Phong-Phu TRAN, Vi-Do NGUYEN, Hoai-Nam PHAM, Hoang-Anh LE-NGOC, Hong-An NGUYEN, Thi-Tuong-Vi HUYNH, Khanh-Linh LE, Ngoc-Bich NGUYEN, Thi-Hiep DAO, Tien-Tuan <p>This dataset include the total of 757 human skulls reconstructed from computed tomography (CT) image sets. The CT image sets were constructed from the three main databases: (1) The Cancer Imaging Archive (DOI: 10.7937/TCIA.HMQ8-J677), (2) The New Mexico Decedent CT Images (DOI: 10.1055/s-0041-1730999), and (3) The MGU500+ (DOI: 10.1016/j.dib.2021.107524). The selected skull image sets have normal and full skull structures. We employed the voxelization technique to reconstruct skull meshes from CT image slices of each subject. The skull meshes were stored in *.off files. For each skull mesh, we estimated the skull shape by estimating the alpha shape of the skull meshes and stored in *.off files. The feature points on the skulls were also manually picked and stored in *.csv files with the form of 16x3 matrices, in which 16 is the number of feature points. A template skull shape was also deformed to the skull shapes of all subjects and stored in *.off files. The deformed skull shapes have unified skull feature points throughout all subjects. Moreover, we also added the source codes of the project. The details of the dataset were presented in the ReadMe.txt file. More details of how to use the source code project and the dataset were presented in the dTwin4SkullShapeTutorials.pdf file.</p> |
| title | dTwin4SkullShapes: a Digital Twin Dataset of Human Skull Shapes for Skull Missing Vs. Existing Part Learning and Prediction |
| url | https://doi.org/10.5281/zenodo.12729483 |